A data scientist works with the meaning of data. He is responsible for the analysis, looks for dependencies, draws conclusions, and builds hypotheses. First of all, the work of a data scientist is to solve specific business problems in data analysis. Secondly, he must be able to find insights that the business has not even thought about yet. Let’s explain with an example.
Imagine that an e-commerce company needs to optimize the use of trucks. You need to predict the number of orders that will arrive in the next month and the dimensions of the boxes for these orders. The data scientist will do data analysis and forecasts.
He works with already processed and structured data that a data engineer has prepared for him. A data scientist may not even know where this data came from, what form it was originally, and what had to be done to process it and bring it to that form.
A data scientist works in systems that a data engineer has installed and configured: databases and data warehouses, tools for data processing, and training ML models. He doesn’t do all of this by hand like a data engineer. Machine learning and artificial intelligence come to his aid: he creates and trains models that help him in his work.
The Main Tasks Of A Data Scientist
- analyzes and explores data to solve business problems;
- looking for hidden patterns that are not visible at first glance (insights);
- creates and trains machine learning models;
- presents information in a business-friendly way.
What You Need To Know and What Tools To Use
- Used languages and ML sandboxes: As for data engineers, Python is one of the main languages for data scientists. R is also often used, so knowledge can also come in handy. Queries to data sources are written in SQL – you also need to know this language. Popular tools include Jupyter Notebook, Airflow, and Zeppelin.
- Mathematics and data analysis: Data analysis is not complete without mathematics and descriptive statistics.
- Machine learning: A data scientist should be able to create models for machine learning and neural networks.
- Data visualization: The analysis results themselves are important, but for company leaders to understand them, a data scientist must prepare them in a convenient format; most often, these are graphs or dashboards.
- Clouds: Data scientists and data engineering specialists should be able to work with cloud platforms. After all, the entire architecture of a company can often be built in the cloud. In addition, clouds facilitate the work of a data scientist. It can use unlimited power for constant experimentation and hypothesis testing. Also, there are various tools for quickly bringing finished models to production on cloud platforms.
Big Data In The Clouds
Large companies collect a huge amount of data measured in petabytes. Often local systems cannot handle this amount of data. First, they need to be stored somewhere, and companies can’t always allocate that much space. But the problem is not so much in data storage but in maintaining the infrastructure for storing it. Companies need to place their data centers somewhere, monitor their temperature, and maintain stability. And outdated equipment needs to be updated. All this is expensive and labor-intensive. Secondly, you need to engage in big data analytics. At the same time, different capacities are required for this in different periods. Clouds allow companies to scale computing power when the need arises and pay for actual consumption.
Analysts from Gartner have suggested that in 2022 cloud computing will become a mandatory part of 90% of new products and services in the field of big data. This approach is called Cloud First: when a company, first of all, seeks to create its infrastructure in the cloud. This makes it possible to quickly launch new projects, test hypotheses, and bring new products to market.
Clouds allow companies to outsource infrastructure issues. All the tasks of building and maintaining systems fall on the shoulders of the cloud provider. The business retains only the competence in business processes for processing data and getting benefits from it. Even if a company is not ready to transfer its data to a public cloud, it can rent capacity and deploy a private cloud. Contractors will maintain the equipment and all systems, and the data will not leave the company’s secure circuit.